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Analyzer - Add failure check feature in data diagnosis (#378) 

**Description**
Add failure check feature in data diagnosis.

**Major Revision**
- Add failure check rule op to support that if there exists metric_regex not been matched by any metric in result, label as failedtest
- Split performance issue and failedtest in categories


**Minor Revision**
- replace DataFrame.append() with pd.concat since append() will be removed in later version of pandas
This commit is contained in:
Yuting Jiang 2022-08-01 12:35:35 +08:00 коммит произвёл GitHub
Родитель ef4d65745b
Коммит ec16d42564
Не найден ключ, соответствующий данной подписи
Идентификатор ключа GPG: 4AEE18F83AFDEB23
11 изменённых файлов: 225 добавлений и 20 удалений
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18
superbench/analyzer/data_diagnosis.py
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@ -62,9 +62,15 @@ class DataDiagnosis(RuleBase):
"""
if metric in baseline:
return baseline[metric]
elif 'return_code' in metric:
return 0
else:
short = metric
# exclude rank info, for example, '.*:\d+'->'.*'
short = metric.strip(metric.split(':')[-1]).strip(':')
if ':' in metric:
short = metric.strip(metric.split(':')[-1]).strip(':')
else:
short = metric.split('/')[0]
if short in baseline:
return baseline[short]
# baseline not defined
@ -106,6 +112,7 @@ class DataDiagnosis(RuleBase):
self._sb_rules = {}
self._enable_metrics = set()
benchmark_rules = rules['superbench']['rules']
self._raw_rules = benchmark_rules
for rule in benchmark_rules:
benchmark_rules[rule] = self._check_and_format_rules(benchmark_rules[rule], rule)
self._sb_rules[rule] = {}
@ -154,6 +161,10 @@ class DataDiagnosis(RuleBase):
violated_num = 0
if rule_op == RuleOp.multi_rules:
violated_num = rule_op(self._sb_rules[rule], details, categories, violation)
elif rule_op == RuleOp.failure_check:
violated_num = rule_op(
data_row, self._sb_rules[rule], summary_data_row, details, categories, self._raw_rules[rule]
)
else:
violated_num = rule_op(data_row, self._sb_rules[rule], summary_data_row, details, categories)
# label the node as defective one
@ -197,7 +208,10 @@ class DataDiagnosis(RuleBase):
details_row, summary_data_row = self._run_diagnosis_rules_for_single_node(node)
if details_row:
data_not_accept_df.loc[node] = details_row
summary_details_df = summary_details_df.append(summary_data_row)
summary_details_df = pd.concat(
[summary_details_df,
pd.DataFrame([summary_data_row.to_dict()], index=[summary_data_row.name])]
)
label_df.loc[node] = 1
else:
label_df.loc[node] = 0

59
superbench/analyzer/diagnosis_rule_op.py
Просмотреть файл

@ -4,6 +4,7 @@
"""A module for data diagnosis rule ops."""
from typing import Dict, Callable
import re
import pandas as pd
@ -17,6 +18,7 @@ class DiagnosisRuleType(Enum):
VARIANCE = 'variance'
VALUE = 'value'
MULTI_RULES = 'multi_rules'
FAILURE_CHECK = 'failure_check'
class RuleOp:
@ -82,7 +84,7 @@ class RuleOp:
"""
# metric not in raw_data or the value is none, miss test
if metric not in data_row or pd.isna(data_row[metric]):
RuleOp.add_categories_and_details(metric + '_miss', rule['categories'], details, categories)
RuleOp.add_categories_and_details(metric + '_miss', 'FailedTest', details, categories)
return True
return False
@ -97,7 +99,8 @@ class RuleOp:
categories (set): set of categories of violated rules
"""
details.append(detail)
categories.add(category)
if category:
categories.add(category)
@staticmethod
def variance(data_row, rule, summary_data_row, details, categories):
@ -122,9 +125,7 @@ class RuleOp:
# every metric should pass the rule
for metric in rule['metrics']:
# metric not in raw_data or the value is none, miss test
if RuleOp.miss_test(metric, rule, data_row, details, categories):
violated_metric_num += 1
else:
if not RuleOp.miss_test(metric, rule, data_row, details, categories):
violate_metric = False
# check if metric pass the rule
val = data_row[metric]
@ -140,7 +141,10 @@ class RuleOp:
info = '(B/L: {:.4f} VAL: {:.4f} VAR: {:.2f}% Rule:{})'.format(
baseline, val, var * 100, rule['criteria']
)
RuleOp.add_categories_and_details(metric + info, rule['categories'], details, categories)
if 'store' not in rule or not rule['store']:
RuleOp.add_categories_and_details(metric + info, rule['categories'], details, categories)
else:
RuleOp.add_categories_and_details(metric + info, None, details, categories)
return violated_metric_num
@staticmethod
@ -167,9 +171,7 @@ class RuleOp:
# every metric should pass the rule
for metric in rule['metrics']:
# metric not in raw_data or the value is none, miss test
if RuleOp.miss_test(metric, rule, data_row, details, categories):
violated_metric_num += 1
else:
if not RuleOp.miss_test(metric, rule, data_row, details, categories):
violate_metric = False
# check if metric pass the rule
val = data_row[metric]
@ -179,7 +181,10 @@ class RuleOp:
if violate_metric:
violated_metric_num += 1
info = '(VAL: {:.4f} Rule:{})'.format(val, rule['criteria'])
RuleOp.add_categories_and_details(metric + info, rule['categories'], details, categories)
if 'store' not in rule or not rule['store']:
RuleOp.add_categories_and_details(metric + info, rule['categories'], details, categories)
else:
RuleOp.add_categories_and_details(metric + info, None, details, categories)
return violated_metric_num
@staticmethod
@ -205,7 +210,41 @@ class RuleOp:
RuleOp.add_categories_and_details(info, rule['categories'], details, categories)
return 1 if violated else 0
@staticmethod
def failure_check(data_row, rule, summary_data_row, details, categories, raw_rule):
"""Rule op function of failure_check.
Args:
data_row (pd.Series): raw data of the metrics
rule (dict): rule including function, criteria, metrics with their baseline values and categories
summary_data_row (pd.Series): results of the metrics processed after the function
details (list): details about violated rules and related data
categories (set): categories of violated rules
raw_rule (dict): raw rule read from rule file
Returns:
number: the number of the metrics that violate the rule if the rule is not passed, otherwise 0
"""
violated_metric_num = 0
for metric_regex in raw_rule['metrics']:
match = False
for metric in rule['metrics']:
if re.search(metric_regex, metric):
match = True
# metric not in raw_data or the value is none, miss test
if metric not in data_row or pd.isna(data_row[metric]):
violated_metric_num += 1
break
# metric_regex written in rules is not matched by any metric, miss test
if not match:
violated_metric_num += 1
RuleOp.add_categories_and_details(metric_regex + '_miss', rule['categories'], details, categories)
# return code != 0, failed test
violated_metric_num += RuleOp.value(data_row, rule, summary_data_row, details, categories)
return violated_metric_num
RuleOp.add_rule_func(DiagnosisRuleType.VARIANCE)(RuleOp.variance)
RuleOp.add_rule_func(DiagnosisRuleType.VALUE)(RuleOp.value)
RuleOp.add_rule_func(DiagnosisRuleType.MULTI_RULES)(RuleOp.multi_rules)
RuleOp.add_rule_func(DiagnosisRuleType.FAILURE_CHECK)(RuleOp.failure_check)

Двоичные данные
superbench/analyzer/diagnosis_summary.xlsx Normal file
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Двоичный файл не отображается.

2
superbench/analyzer/file_handler.py
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@ -34,7 +34,7 @@ def read_raw_data(raw_data_path):
try:
with p.open(encoding='utf-8') as f:
for single_node_summary in jsonlines.Reader(f):
raw_data_df = raw_data_df.append(single_node_summary, ignore_index=True)
raw_data_df = pd.concat([raw_data_df, pd.DataFrame([single_node_summary])], axis=0, ignore_index=True)
raw_data_df = raw_data_df.rename(raw_data_df['node'])
raw_data_df = raw_data_df.drop(columns=['node'])
except Exception as e:

46
tests/analyzer/test_data_diagnosis.py
Просмотреть файл

@ -358,3 +358,49 @@ class TestDataDiagnosis(unittest.TestCase):
+ 'mem-bw/D2H_Mem_BW(B/L: 24.3000 VAL: 10.0000 VAR: -58.85% Rule:lambda x:x<-0.5),' +
'rule3:lambda label:True if label["rule1"]+label["rule2"]>=2 else False'
)
def test_failure_check(self):
"""Test failure test check feature."""
diag1 = DataDiagnosis()
# test _run_diagnosis_rules_for_single_node
rules = {
'superbench': {
'rules': {
'rule1': {
'categories':
'FailedTest',
'criteria':
'lambda x:x!=0',
'function':
'failure_check',
'metrics': [
'gemm-flops/return_code:0', 'gemm-flops/return_code:1', 'gemm-flops/return_code:2',
'resnet_models/pytorch-resnet152/return_code'
]
}
}
}
}
baseline = {}
data = {
'gemm-flops/return_code:0': [0, -1],
'gemm-flops/return_code:1': [0, pd.NA],
'resnet_models/pytorch-resnet152/return_code': [0, -1]
}
diag1._raw_data_df = pd.DataFrame(data, index=['sb-validation-04', 'sb-validation-05'])
diag1._benchmark_metrics_dict = diag1._get_metrics_by_benchmarks(list(diag1._raw_data_df.columns))
diag1._parse_rules_and_baseline(rules, baseline)
(details_row, summary_data_row) = diag1._run_diagnosis_rules_for_single_node('sb-validation-04')
assert (details_row)
assert ('FailedTest' in details_row[0])
assert (details_row[1] == 'gemm-flops/return_code:2_miss')
(details_row, summary_data_row) = diag1._run_diagnosis_rules_for_single_node('sb-validation-05')
assert (details_row)
assert ('FailedTest' in details_row[0])
assert (
details_row[1] == 'gemm-flops/return_code:0(VAL: -1.0000 Rule:lambda x:x!=0),' +
'gemm-flops/return_code:1_miss,' + 'gemm-flops/return_code:2_miss,' +
'resnet_models/pytorch-resnet152/return_code(VAL: -1.0000 Rule:lambda x:x!=0)'
)

112
tests/analyzer/test_ruleop.py
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@ -137,7 +137,7 @@ class TestRuleOp(unittest.TestCase):
true_baselines = [
{
'name': 'rule1',
'categories': 'CNN',
'categories': 'TMP',
'criteria': 'lambda x:x<-0.5',
'store': True,
'function': 'variance',
@ -146,7 +146,7 @@ class TestRuleOp(unittest.TestCase):
}
}, {
'name': 'rule2',
'categories': 'CNN',
'categories': 'TMP',
'criteria': 'lambda x:x<-0.5',
'store': True,
'function': 'variance',
@ -155,7 +155,7 @@ class TestRuleOp(unittest.TestCase):
}
}, {
'name': 'rule3',
'categories': 'KernelLaunch',
'categories': 'CNN',
'criteria': 'lambda label:True if label["rule1"]+label["rule2"]>=2 else False',
'store': False,
'function': 'multi_rules'
@ -187,6 +187,7 @@ class TestRuleOp(unittest.TestCase):
rule_op = RuleOp.get_rule_func(DiagnosisRuleType(true_baselines[2]['function']))
violated_metric_num = rule_op(true_baselines[2], details, categories, label)
assert (violated_metric_num)
assert ('TMP' not in categories)
assert ('CNN' in categories)
assert (
details == [
@ -197,3 +198,108 @@ class TestRuleOp(unittest.TestCase):
'rule3:lambda label:True if label["rule1"]+label["rule2"]>=2 else False'
]
)
def test_failure_check_op(self):
"""Test for failure_check op."""
details = []
categories = set()
data_row = pd.Series()
summary_data_row = pd.Series(dtype=float)
# invalid rule
false_baselines = [{'categories': 'FailedTest', 'criteria': 'lambda x:x!=0', 'function': 'failure_check'}]
label = {}
for rule in false_baselines:
self.assertRaises(
Exception, RuleOp.failure_check, data_row, rule, summary_data_row, details, categories, rule
)
true_baselines = [
{
'name': 'rule1',
'categories': 'FailedTest',
'criteria': 'lambda x:x!=0',
'function': 'failure_check',
'metrics': {
'gemm-flops/return_code:0': -1,
'gemm-flops/return_code:1': -1,
'resnet_models/pytorch-resnet152/return_code': -1,
}
}, {
'name': 'rule2',
'categories': 'FailedTest',
'criteria': 'lambda x:x!=0',
'function': 'failure_check',
'metrics': {
'gemm-flops/return_code:0': -1,
'gemm-flops/return_code:1': -1,
'gemm-flops/return_code:2': -1,
'resnet_models/pytorch-resnet152/return_code': -1,
}
}
]
# positive case
data = {
'gemm-flops/return_code:0': 0,
'gemm-flops/return_code:1': 0,
'resnet_models/pytorch-resnet152/return_code': 0,
}
data_row = pd.Series(data)
rule_op = RuleOp.get_rule_func(DiagnosisRuleType(true_baselines[0]['function']))
label[true_baselines[0]['name']
] = rule_op(data_row, true_baselines[0], summary_data_row, details, categories, true_baselines[0])
assert (label[true_baselines[0]['name']] == 0)
# negative cases
# 1. return_code != 0
data = {
'gemm-flops/return_code:0': 0,
'gemm-flops/return_code:1': -1,
'resnet_models/pytorch-resnet152/return_code': -1,
}
details = []
categories = set()
data_row = pd.Series(data)
rule_op = RuleOp.get_rule_func(DiagnosisRuleType(true_baselines[0]['function']))
label[true_baselines[0]['name']
] = rule_op(data_row, true_baselines[0], summary_data_row, details, categories, true_baselines[0])
assert (label[true_baselines[0]['name']] != 0)
assert ({'FailedTest'} == categories)
assert (
details == [
'gemm-flops/return_code:1(VAL: -1.0000 Rule:lambda x:x!=0)',
'resnet_models/pytorch-resnet152/return_code(VAL: -1.0000 Rule:lambda x:x!=0)',
]
)
# 2. metric not in raw_data or the value is none, miss test
data = {
'gemm-flops/return_code:0': 0,
'gemm-flops/return_code:1': 0,
'resnet_models/pytorch-resnet152/return_code': 0,
}
details = []
categories = set()
data_row = pd.Series(data)
rule_op = RuleOp.get_rule_func(DiagnosisRuleType(true_baselines[0]['function']))
label[true_baselines[1]['name']
] = rule_op(data_row, true_baselines[1], summary_data_row, details, categories, true_baselines[1])
assert (label[true_baselines[1]['name']] != 0)
assert ({'FailedTest'} == categories)
assert (details == ['gemm-flops/return_code:2_miss'])
# 3. metric_regex written in rules is not matched by any metric, miss test
data = {
'gemm-flops/return_code:0': 0,
'gemm-flops/return_code:1': 0,
'resnet_models/pytorch-resnet152/return_code': 0,
}
details = []
categories = set()
data_row = pd.Series(data)
rule_op = RuleOp.get_rule_func(DiagnosisRuleType(true_baselines[0]['function']))
label[true_baselines[1]['name']
] = rule_op(data_row, true_baselines[0], summary_data_row, details, categories, true_baselines[1])
assert (label[true_baselines[1]['name']] != 0)
assert ({'FailedTest'} == categories)
assert (details == ['gemm-flops/return_code:2_miss'])

2
tests/data/diagnosis_summary.html
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@ -82,7 +82,7 @@
</tr>
<tr>
<td>sb-validation-03</td>
<td>FailedTest,Mem</td>
<td>FailedTest</td>
<td>mem-bw/D2H_Mem_BW:0_miss,mem-bw/D2H_Mem_BW:1_miss,mem-bw/D2H_Mem_BW:2_miss,mem-bw/D2H_Mem_BW:3_miss,mem-bw/D2H_Mem_BW:4_miss,mem-bw/D2H_Mem_BW:5_miss,mem-bw/D2H_Mem_BW:6_miss,mem-bw/D2H_Mem_BW:7_miss,mem-bw/H2D_Mem_BW:0_miss,mem-bw/H2D_Mem_BW:1_miss,mem-bw/H2D_Mem_BW:2_miss,mem-bw/H2D_Mem_BW:3_miss,mem-bw/H2D_Mem_BW:4_miss,mem-bw/H2D_Mem_BW:5_miss,mem-bw/H2D_Mem_BW:6_miss,mem-bw/H2D_Mem_BW:7_miss,mem-bw/return_code(VAL: 1.0000 Rule:lambda x:x&gt;0)</td>
<td>0.0%</td>
<td>-0.17%</td>

2
tests/data/diagnosis_summary.json
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@ -1402,7 +1402,7 @@
"vgg_models/pytorch-vgg19/throughput_train_float16": 709.1127328377,
"Accept": false,
"#Issues": 17.0,
"Category": "FailedTest,Mem",
"Category": "FailedTest",
"Issue_Details": "mem-bw/D2H_Mem_BW:0_miss,mem-bw/D2H_Mem_BW:1_miss,mem-bw/D2H_Mem_BW:2_miss,mem-bw/D2H_Mem_BW:3_miss,mem-bw/D2H_Mem_BW:4_miss,mem-bw/D2H_Mem_BW:5_miss,mem-bw/D2H_Mem_BW:6_miss,mem-bw/D2H_Mem_BW:7_miss,mem-bw/H2D_Mem_BW:0_miss,mem-bw/H2D_Mem_BW:1_miss,mem-bw/H2D_Mem_BW:2_miss,mem-bw/H2D_Mem_BW:3_miss,mem-bw/H2D_Mem_BW:4_miss,mem-bw/H2D_Mem_BW:5_miss,mem-bw/H2D_Mem_BW:6_miss,mem-bw/H2D_Mem_BW:7_miss,mem-bw/return_code(VAL: 1.0000 Rule:lambda x:x>0)",
"Index": "sb-validation-03"
}

2
tests/data/diagnosis_summary.jsonl
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@ -1,2 +1,2 @@
{"Category": "KernelLaunch", "Defective Details": "kernel-launch/event_overhead:0(B/L: 0.0060 VAL: 0.1000 VAR: 1577.85% Rule:lambda x:x>0.05)", "kernel-launch/event_overhead:0": 15.7785234899, "kernel-launch/event_overhead:1": -0.0016778523, "kernel-launch/event_overhead:2": -0.0654362416, "kernel-launch/event_overhead:3": -0.0771812081, "kernel-launch/event_overhead:4": -0.0067114094, "kernel-launch/event_overhead:5": -0.0117449664, "kernel-launch/event_overhead:6": -0.0402684564, "kernel-launch/event_overhead:7": -0.0100671141, "kernel-launch/return_code": 0.0, "kernel-launch/wall_overhead:0": 0.0, "kernel-launch/wall_overhead:1": 0.0, "kernel-launch/wall_overhead:2": 0.0194931774, "kernel-launch/wall_overhead:3": 0.022417154, "kernel-launch/wall_overhead:4": 0.0360623782, "kernel-launch/wall_overhead:5": -0.0194931774, "kernel-launch/wall_overhead:6": 0.0185185185, "kernel-launch/wall_overhead:7": 0.0438596491, "mem-bw/D2H_Mem_BW:0": 0.0, "mem-bw/D2H_Mem_BW:1": 0.012345679, "mem-bw/D2H_Mem_BW:2": 0.0082304527, "mem-bw/D2H_Mem_BW:3": 0.012345679, "mem-bw/D2H_Mem_BW:4": 0.0, "mem-bw/D2H_Mem_BW:5": 0.0, "mem-bw/D2H_Mem_BW:6": -0.0164609053, "mem-bw/D2H_Mem_BW:7": 0.012345679, "mem-bw/H2D_Mem_BW:0": 0.0, "mem-bw/H2D_Mem_BW:1": 0.0078125, "mem-bw/H2D_Mem_BW:2": 0.015625, "mem-bw/H2D_Mem_BW:3": 0.01953125, "mem-bw/H2D_Mem_BW:4": 0.0234375, "mem-bw/H2D_Mem_BW:5": 0.0078125, "mem-bw/H2D_Mem_BW:6": -0.01171875, "mem-bw/H2D_Mem_BW:7": 0.01953125, "mem-bw/return_code": 0.0, "Index": "sb-validation-01"}
{"Category": "FailedTest,Mem", "Defective Details": "mem-bw/D2H_Mem_BW:0_miss,mem-bw/D2H_Mem_BW:1_miss,mem-bw/D2H_Mem_BW:2_miss,mem-bw/D2H_Mem_BW:3_miss,mem-bw/D2H_Mem_BW:4_miss,mem-bw/D2H_Mem_BW:5_miss,mem-bw/D2H_Mem_BW:6_miss,mem-bw/D2H_Mem_BW:7_miss,mem-bw/H2D_Mem_BW:0_miss,mem-bw/H2D_Mem_BW:1_miss,mem-bw/H2D_Mem_BW:2_miss,mem-bw/H2D_Mem_BW:3_miss,mem-bw/H2D_Mem_BW:4_miss,mem-bw/H2D_Mem_BW:5_miss,mem-bw/H2D_Mem_BW:6_miss,mem-bw/H2D_Mem_BW:7_miss,mem-bw/return_code(VAL: 1.0000 Rule:lambda x:x>0)", "kernel-launch/event_overhead:0": 0.0, "kernel-launch/event_overhead:1": -0.0016778523, "kernel-launch/event_overhead:2": -0.0654362416, "kernel-launch/event_overhead:3": -0.0771812081, "kernel-launch/event_overhead:4": -0.0067114094, "kernel-launch/event_overhead:5": -0.0117449664, "kernel-launch/event_overhead:6": -0.0402684564, "kernel-launch/event_overhead:7": -0.0100671141, "kernel-launch/return_code": 0.0, "kernel-launch/wall_overhead:0": 0.0, "kernel-launch/wall_overhead:1": 0.0, "kernel-launch/wall_overhead:2": 0.0194931774, "kernel-launch/wall_overhead:3": 0.022417154, "kernel-launch/wall_overhead:4": 0.0360623782, "kernel-launch/wall_overhead:5": -0.0194931774, "kernel-launch/wall_overhead:6": 0.0185185185, "kernel-launch/wall_overhead:7": 0.0438596491, "mem-bw/D2H_Mem_BW:0": null, "mem-bw/D2H_Mem_BW:1": null, "mem-bw/D2H_Mem_BW:2": null, "mem-bw/D2H_Mem_BW:3": null, "mem-bw/D2H_Mem_BW:4": null, "mem-bw/D2H_Mem_BW:5": null, "mem-bw/D2H_Mem_BW:6": null, "mem-bw/D2H_Mem_BW:7": null, "mem-bw/H2D_Mem_BW:0": null, "mem-bw/H2D_Mem_BW:1": null, "mem-bw/H2D_Mem_BW:2": null, "mem-bw/H2D_Mem_BW:3": null, "mem-bw/H2D_Mem_BW:4": null, "mem-bw/H2D_Mem_BW:5": null, "mem-bw/H2D_Mem_BW:6": null, "mem-bw/H2D_Mem_BW:7": null, "mem-bw/return_code": 1.0, "Index": "sb-validation-03"}
{"Category": "FailedTest", "Defective Details": "mem-bw/D2H_Mem_BW:0_miss,mem-bw/D2H_Mem_BW:1_miss,mem-bw/D2H_Mem_BW:2_miss,mem-bw/D2H_Mem_BW:3_miss,mem-bw/D2H_Mem_BW:4_miss,mem-bw/D2H_Mem_BW:5_miss,mem-bw/D2H_Mem_BW:6_miss,mem-bw/D2H_Mem_BW:7_miss,mem-bw/H2D_Mem_BW:0_miss,mem-bw/H2D_Mem_BW:1_miss,mem-bw/H2D_Mem_BW:2_miss,mem-bw/H2D_Mem_BW:3_miss,mem-bw/H2D_Mem_BW:4_miss,mem-bw/H2D_Mem_BW:5_miss,mem-bw/H2D_Mem_BW:6_miss,mem-bw/H2D_Mem_BW:7_miss,mem-bw/return_code(VAL: 1.0000 Rule:lambda x:x>0)", "kernel-launch/event_overhead:0": 0.0, "kernel-launch/event_overhead:1": -0.0016778523, "kernel-launch/event_overhead:2": -0.0654362416, "kernel-launch/event_overhead:3": -0.0771812081, "kernel-launch/event_overhead:4": -0.0067114094, "kernel-launch/event_overhead:5": -0.0117449664, "kernel-launch/event_overhead:6": -0.0402684564, "kernel-launch/event_overhead:7": -0.0100671141, "kernel-launch/return_code": 0.0, "kernel-launch/wall_overhead:0": 0.0, "kernel-launch/wall_overhead:1": 0.0, "kernel-launch/wall_overhead:2": 0.0194931774, "kernel-launch/wall_overhead:3": 0.022417154, "kernel-launch/wall_overhead:4": 0.0360623782, "kernel-launch/wall_overhead:5": -0.0194931774, "kernel-launch/wall_overhead:6": 0.0185185185, "kernel-launch/wall_overhead:7": 0.0438596491, "mem-bw/D2H_Mem_BW:0": null, "mem-bw/D2H_Mem_BW:1": null, "mem-bw/D2H_Mem_BW:2": null, "mem-bw/D2H_Mem_BW:3": null, "mem-bw/D2H_Mem_BW:4": null, "mem-bw/D2H_Mem_BW:5": null, "mem-bw/D2H_Mem_BW:6": null, "mem-bw/D2H_Mem_BW:7": null, "mem-bw/H2D_Mem_BW:0": null, "mem-bw/H2D_Mem_BW:1": null, "mem-bw/H2D_Mem_BW:2": null, "mem-bw/H2D_Mem_BW:3": null, "mem-bw/H2D_Mem_BW:4": null, "mem-bw/H2D_Mem_BW:5": null, "mem-bw/H2D_Mem_BW:6": null, "mem-bw/H2D_Mem_BW:7": null, "mem-bw/return_code": 1.0, "Index": "sb-validation-03"}

2
tests/data/diagnosis_summary.md
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@ -1,4 +1,4 @@
| machine | Category | Defective Details | kernel-launch/event_overhead:0 | kernel-launch/event_overhead:1 | kernel-launch/event_overhead:2 | kernel-launch/event_overhead:3 | kernel-launch/event_overhead:4 | kernel-launch/event_overhead:5 | kernel-launch/event_overhead:6 | kernel-launch/event_overhead:7 | kernel-launch/return_code | kernel-launch/wall_overhead:0 | kernel-launch/wall_overhead:1 | kernel-launch/wall_overhead:2 | kernel-launch/wall_overhead:3 | kernel-launch/wall_overhead:4 | kernel-launch/wall_overhead:5 | kernel-launch/wall_overhead:6 | kernel-launch/wall_overhead:7 | mem-bw/D2H_Mem_BW:0 | mem-bw/D2H_Mem_BW:1 | mem-bw/D2H_Mem_BW:2 | mem-bw/D2H_Mem_BW:3 | mem-bw/D2H_Mem_BW:4 | mem-bw/D2H_Mem_BW:5 | mem-bw/D2H_Mem_BW:6 | mem-bw/D2H_Mem_BW:7 | mem-bw/H2D_Mem_BW:0 | mem-bw/H2D_Mem_BW:1 | mem-bw/H2D_Mem_BW:2 | mem-bw/H2D_Mem_BW:3 | mem-bw/H2D_Mem_BW:4 | mem-bw/H2D_Mem_BW:5 | mem-bw/H2D_Mem_BW:6 | mem-bw/H2D_Mem_BW:7 | mem-bw/return_code |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| sb-validation-01 | KernelLaunch | kernel-launch/event_overhead:0(B/L: 0.0060 VAL: 0.1000 VAR: 1577.85% Rule:lambda x:x>0.05) | 1577.85% | -0.17% | -6.54% | -7.72% | -0.67% | -1.17% | -4.03% | -1.01% | 0.0 | 0.0% | 0.0% | 1.95% | 2.24% | 3.61% | -1.95% | 1.85% | 4.39% | 0.0% | 1.23% | 0.82% | 1.23% | 0.0% | 0.0% | -1.65% | 1.23% | 0.0% | 0.78% | 1.56% | 1.95% | 2.34% | 0.78% | -1.17% | 1.95% | 0.0 |
| sb-validation-03 | FailedTest,Mem | mem-bw/D2H_Mem_BW:0_miss,mem-bw/D2H_Mem_BW:1_miss,mem-bw/D2H_Mem_BW:2_miss,mem-bw/D2H_Mem_BW:3_miss,mem-bw/D2H_Mem_BW:4_miss,mem-bw/D2H_Mem_BW:5_miss,mem-bw/D2H_Mem_BW:6_miss,mem-bw/D2H_Mem_BW:7_miss,mem-bw/H2D_Mem_BW:0_miss,mem-bw/H2D_Mem_BW:1_miss,mem-bw/H2D_Mem_BW:2_miss,mem-bw/H2D_Mem_BW:3_miss,mem-bw/H2D_Mem_BW:4_miss,mem-bw/H2D_Mem_BW:5_miss,mem-bw/H2D_Mem_BW:6_miss,mem-bw/H2D_Mem_BW:7_miss,mem-bw/return_code(VAL: 1.0000 Rule:lambda x:x>0) | 0.0% | -0.17% | -6.54% | -7.72% | -0.67% | -1.17% | -4.03% | -1.01% | 0.0 | 0.0% | 0.0% | 1.95% | 2.24% | 3.61% | -1.95% | 1.85% | 4.39% | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 1.0 |
| sb-validation-03 | FailedTest | mem-bw/D2H_Mem_BW:0_miss,mem-bw/D2H_Mem_BW:1_miss,mem-bw/D2H_Mem_BW:2_miss,mem-bw/D2H_Mem_BW:3_miss,mem-bw/D2H_Mem_BW:4_miss,mem-bw/D2H_Mem_BW:5_miss,mem-bw/D2H_Mem_BW:6_miss,mem-bw/D2H_Mem_BW:7_miss,mem-bw/H2D_Mem_BW:0_miss,mem-bw/H2D_Mem_BW:1_miss,mem-bw/H2D_Mem_BW:2_miss,mem-bw/H2D_Mem_BW:3_miss,mem-bw/H2D_Mem_BW:4_miss,mem-bw/H2D_Mem_BW:5_miss,mem-bw/H2D_Mem_BW:6_miss,mem-bw/H2D_Mem_BW:7_miss,mem-bw/return_code(VAL: 1.0000 Rule:lambda x:x>0) | 0.0% | -0.17% | -6.54% | -7.72% | -0.67% | -1.17% | -4.03% | -1.01% | 0.0 | 0.0% | 0.0% | 1.95% | 2.24% | 3.61% | -1.95% | 1.85% | 4.39% | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 1.0 |

Двоичные данные
tests/data/diagnosis_summary.xlsx
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